Building a data warehouse is a very complicated task involving business needs, data integration, scalability, performance and flexibility. Data architects with experience can still fall short, and their missteps along the way can result in problems later on. Knowing some of the most common pitfalls that data teams face can help them avoid headaches and deliver more successful data warehousing programs. In this article, you’ll learn about the key mistakes to avoid when architecting a new data warehouse solution.
Not Getting Clear on Business Requirements
Many data analytics services initiatives fail because the goals and business uses were never clearly defined upfront. Designers of these services often dive into the technical details without stepping back to understand exactly how various departments and roles intend to consume information. This oversight leads to disjointed systems that miss the mark for actually supporting business objectives.
Before starting the design process, data teams should spend time deeply analyzing business requirements through discussions with key stakeholders in various business units. The key is to not just understand their general goals but to map out their specific analytics and reporting needs. This becomes the foundation for a data model that serves the business rather than a technical exercise that fails to deliver business value.
Lack of Focus on Data Quality
The old adage “garbage in, garbage out” rings especially true for data warehousing programs. No matter how elegant the data model or how performant the technology stack, poor data quality will undermine the usefulness of the data warehouse. Users will quickly lose trust in the information if it contains inaccurate, incomplete, or meaningless data.
Data architects must bake data quality practices into their overall data integration workflows and data governance strategy. This includes:
- Profile data at the source systems to understand completeness and accuracy issues.
- Standardize data formats during ETL/ELT.
- Perform integrity checks for mandatory attributes.
- Monitor data quality KPIs on an ongoing basis.
Catching and resolving data problems early in the process is crucial for maintaining information integrity.
Not Planning for Scalability
Most data warehouse initiatives start small in scope – focused on a specific business unit’s needs or a targeted analytics use case. However, once business users get a taste of the value of integrated, trustworthy enterprise data, their appetite for more data and expanded use cases tend to grow quickly. Data teams like ai in investment banking companies often doesn’t architect their data pipelines and data warehouse technology stack to efficiently scale with increasing data volumes and complexity.
As data warehouse programs scale, data warehouse programs will want to implement technologies that can scale out of the box to avoid scalability bottlenecks down the road. Cloud data warehouses and cloud-native data integration tools offer much more flexibility to seamlessly scale-out infrastructure compared to traditional on-prem solutions. Teams should also consider potential growth areas ahead of time and over-provision storage and computing capacity.
Lack of Reusability in Design
Data teams shouldn’t have to start from scratch every time new analytics needs emerge. Data warehouses with good design plans for the reusability of core data objects like schemas/models so that new use cases don’t have to recreate large parts of the data.
Reuse is made easier by institutionalizing data modeling standards and thinking about broader enterprise data model (not just some departments’) needs. In addition, data teams should also deploy data virtualization and data catalog solutions to uncover and utilize the existing data assets rather than spending time continuously reengineering ETL logic.
Not Considering Multi-Cloud
Many organizations opt to standardize their core data warehouse on a single public cloud provider (like AWS, Azure, or GCP) for simplicity’s sake. But this approach can have its downsides, with the cloud vendor lock-in issue. What if that provider goes down unexpectedly or their pricing changes dramatically?
The data platform of a savvy data team is a multi-cloud strategy. It could be as simple as establishing their primary data warehouse with one vendor and setting up smaller replicated data marts on a different cloud. Or their core data pipeline orchestration layer to write to multiple cloud data warehouses in parallel. It balances risk and cost across cloud providers while still taking advantage of all the cloud can provide.
Lack of Enterprise Data Sharing Strategy
Data warehouses shouldn’t exist in isolation. To drive maximum business value, they need to serve the enterprise’s broader data-sharing needs. This means enabling direct access to data warehouse information for key consumer systems like sales/CRM applications, marketing automation tools, financial planning systems, and other operational systems. Data teams often overlook this integration need or tack it on as an afterthought.
The key is building APIs and microservices for core data warehouse objects like customer 360, product, asset, cost, and financial data. This facilitates real-time tapping of data warehouse information versus requiring teams to replicate data or build separate models. Strong data governance and security controls are also crucial to enable responsible data sharing.
Separate Analytics Sandboxes
Individual data science and analytics teams will often create their own standalone data repositories, models, and pipelines for their projects. This leads to fragmented siloed efforts versus leveraging the common enterprise data assets. It also makes applying consistent governance, security and data quality practices very difficult.
To do this, data teams should enable the creation of analytics sandboxes and data science workbenches using core, curated data from a central data warehouse. With this approach, the data remains consistent, while the analysts are able to enrich and manipulate data for use of a particular case. This will not only reduce data silos and help streamline workflows but will also enable better quality models built around consistent datasets such as Customer 360, product, sales, and marketing data. Furthermore, real-time analytics, of which $2.6 trillion of revenue can be generated globally, reiterates the point of ua nified data management strategy.
Lack of Metadata Strategy
Data warehouses contain a wealth of technical, business, and operational metadata spanning source systems, ETL jobs, data models, access logs, etc. Managing and harnessing all this metadata is key for driving value from analytics investments – enabling discovery, lineage, cataloging, and more. Unfortunately most data warehouse programs give little thought to their metadata strategy.
Data teams should assess early on what metadata they need to capture across the data lifecycle and select accompanying tools and repositories for storing, governing and accessing this metadata. This could include data catalogs, metadata hubs, model registries, and more. Rich, accessible metadata unlocks the full power of the underlying data.
Conclusion
To be successful in the data warehouse, you need to avoid common design pitfalls. With upfront planning and constant vigilance, data teams are able to avoid having issues around unclear business needs, poor data quality, lack of scalability, and integration complexity. Smaller data warehouses may be able to grow into enterprise data warehouses. Organizations can also realize long-term analytics ROI by managing sound data, governance and metadata strategies. What other data warehouse risks have you encountered, and how do you control them?
